Update README.md

README.md

@@ -31,7 +31,7 @@ Once you have downloaded the datasets, please update the corresponding paths at
 Additionally, for EMBED you will need to preprocess the original dataframes with our script `data_handling/csv_generation_code/generate_embed_csv.ipynb`. Similarly for RSNA please run first `data_handling/csv_generation_code/rsna_generate_full_csv.py`.
 
 
-## Full workflow example for training and evaluating CF-SimCLR
+## Full workflow example for training and evaluating CF-SimCLR on mammography
 Here we'll run through an example to train and evaluate CF-SimCLR on EMBED
 
 1. Train a counterfactual image generation model with 
@@ -46,7 +46,7 @@ python causal_models/save_embed_scanner_cf.py
 
 3. Train the CF-SimCLR model
 ``` 
-python classification/train.py experiment=simclr_embed data.use_counterfactuals=True counterfactual_contrastive=True
+python classification/train.py experiment=simclr_embed data.use_counterfactuals=True data.counterfactual_contrastive=True
 ```
 Alternatively to train a SimCLR baseline just run
 ```